Biometrics is the technology for recognizing physical features, such as a fingerprint, a face, an iris, a vein, and the like, which are different from person to person. Such physical features cannot be stolen or copied by others like a key or password and may be utilized in the security field or the like since they are not at the risk of being changed or lost. Face recognition is a type of biometric technology that includes a technique of detecting a face region in a video or a picture image and identifying the identity of a face included in the detected face region. Such face recognition technology can be utilized in not only the security field, but also a variety of applications in line with the progress during the smart phone age.
Specifically, face recognition is a technique for identifying a face in a detected facial image by using positions of feature points. The feature points may include a center point of an eye, both end points of each eye, both end points and a center point of an eyebrow, both end points of a lip or the like.
Techniques such as a histogram, principal component analysis (PCA), and Adaboost learning algorithm are used for detecting positions of such face feature points, and these methods generally provide good results to some extent when they are applied to ordinary facial images (i.e., normal facial images).
However, there is a problem in that considerable degradation in performance is observed in detecting positions of feature points when these methods are applied to unusual facial images (e.g., a facial image of a person wearing glasses, a facial image with a portion of a face hidden behind hairs, a facial image of a person with exaggerated facial expressions, a non-frontal facial image, a partially dark facial image, an image with closed eyes, or the like).
Accordingly, it is necessary to develop a technique that guarantees consistent performance in detecting positions of feature points of unusual facial images as well as ordinary facial images.